Polymerized Feature-based Domain Adaptation for Cervical Cancer Dose Map Prediction

Abstract

Recently, deep learning (DL) has automated and accelerated the clinical radiation therapy (RT) planning significantly by predicting accurate dose maps. However, most DL-based dose map prediction methods are data-driven and not applicable for cervical cancer where only a small amount of data is available. To address this problem, this paper proposes to transfer the rich knowledge learned from another cancer, i.e., rectum cancer, which has the same scanning area and more clinically available data, to improve the dose map prediction performance for cervical cancer through domain adaptation. In order to close the congenital domain gap between the source (i.e., rectum cancer) and the target (i.e., cervical cancer) domains, we develop an effective Transformer-based polymerized feature module (PFM), which can generate an optimal polymerized feature distribution to smoothly align the two input distributions. Experimental results on two in-house clinical datasets demonstrate the superiority of the proposed method compared with state-of-the-art methods.